The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 207–214, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-207-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 207–214, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-207-2021

  28 Jun 2021

28 Jun 2021

BIM ON ARTIFICIAL INTELLIGENCE FOR DECISION SUPPORT IN E-HEALTH

B. Plaß, C. Prudhomme, and J. J. Ponciano B. Plaß et al.
  • i3mainz, Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, Germany

Keywords: Decision Support System, Knowledge Base, Building Information Modeling, Artificial Intelligence, Care 4.0, Point Cloud, Semantic Segmentation, Object Detection

Abstract. Looking ahead of 2070, the number of the elderly population will increase rapidly in the European Union and beyond. As society ages, it will be confronted to novel challenges related with other concerns like the concept aging in place that the majority of the elderly prefer. Concerning that, the living space must be adapted to the requirements of people with a disability, to support their relatives or friends that will become more and more important in future due to a lack of professional’s and both overstressed and expensive hospitals or nursing homes. Compounding this, those living space requirements are highly individual, depending on the disease. Our study focuses on a medical white box decision support system providing advice even for unknowledgeable users by evaluating the suitability of an elderly’s living environment in terms of their individual disease. In this paper, we propose tackling this issue with a decision support system linked to Building Information Modeling (BIM) and based on Artificial Intelligence using semantic technologies. The proposed approach's contribution is a reliable process that uses up-to-date 3D point cloud data of the person’s living environment and predicts suitable, non-suitable and adaptable zones therein according to different pathologies using formalised knowledge. We are able to provide deep expert knowledge linked from different domains inside a knowledge base and thus produce an outcome through BIM, which is understandable and helpful for two types of users, ordinary people concerned by the matter and building experts. We illustrate our methodology by a proof of concept concerning a wheel-chaired person.